Online handwritten Eastern Asian character recognition is a useful feature for mobile computing devices, such as Tablet PCs, mobile phones and PDAs. Many character recognition systems use a Hidden Markov Model (HMM) approach to recognize characters based on stochastic time sequential data; noting that HMM approaches have been used for years in speech recognition, which inherently rely on temporal information. An individual HMM includes states and state transitions that can be trained using appropriate training information. A group of trained HMMs and input information (e.g., online character information) can be used to predict a probable outcome for the input information (e.g., a character corresponding to the character information).
To apply a HMM approach to online Eastern Asian character recognition, a character sample is represented as time sequential data according to a set of “online features.” More specifically, a process sometimes referred to as “feature extraction” is applied to online ink data to provide corresponding feature information. Given such information, a training process can build trained HMM models for use in online character recognition.
For online character recognition, feature extraction is applied to online ink data for a character and the resulting feature information is input to the trained HMMs. Next, the output from the trained HMMs is used to select a character that corresponds to the feature information and, indirectly, to the online ink data. Accuracy of the trained HMM models depends on a variety of factors, including the selected set of online features. In general, the selected set of online features should be rich enough to encode handwritten Eastern Asia characters, and effective to recognize various characters. Various techniques described herein pertain to designing useful online features fitting a HMM modeling approach.